(147b) Adacta: A Research Data Management Solution with a Focus on Traceability

Industry 4.0 is in part focused on the digitization of equipment and processes, including the development of digital twins that accurately capture key characteristics of their physical counterparts. In catalyst testing (as is the case in other experimental fields), the test stand configuration contains many important details that are necessary to save in order to fully understand the data that are generated. A digital twin, which contains all the data and metadata necessary to fully represent a physical object or system, is an ideal Research Data Management (RDM) tool. This is because a digital twin can accurately reconstruct exactly what was being tested and which devices were used to collect data. Incorporating digital twins into a catalyst testing RDM system ensures that the data are stored alongside the appropriate metadata so that the experimental results are understandable far into the future. Recognizing the benefits of digital twins for enhancing data reuse, omegadot developed Adacta [1], a novel RDM system designed to create a digital twin of an experimental system, with features specifically incorporated to handle the unique complexities associated with catalyst testing.

One such complexity is the fact that catalyst test stands - either the equipment and/or what is being tested – is often changing. However, keeping a digital twin up to date becomes complicated when the physical twin is constantly evolving. This is often the case with catalyst test stands, which are typically designed to evaluate many different samples. Further complicating the development of digital twins, catalyst test stands are typically reconfigured at various times in order to allow the collection of data under vastly different conditions or even chemistries. As a result, the digital twin of a catalyst test stand today can be vastly different from the digital twin of the same catalyst test stand tomorrow.

Adacta addresses this challenge by organizing data and metadata around the timeline, allowing the exact configuration of a test stand at any given point in time to be saved. In this way, the digital twin remains updated even as new catalysts are tested and the test stand evolves. Equipment changeouts, calibrations, and even new process flow configurations are readily traceable with Adacta. This novel RDM approach thus allows data to be placed in the context of the equipment configuration at the time the results were collected. Moreover, the graphical user interface allows one to locate and visualize data, equipment configurations, and installed test samples in intuitive and flexible ways, such as timeline views, data plots, and annotated process flow diagrams.

Conventional catalysis RDM meanwhile often relies on a “forms-based approach,” such as a spreadsheet that contains rigid, predefined, fixed fields for data input and output. While it is technically possible to design a form that can capture the information from the test stand, there are several aspects that make this approach impractical in many situations. For example, it is often difficult to design the form in an unambiguous way. Suppose there is a test stand with multiple temperature sensors installed, and these sensors can be placed in different positions over the life of the system. Labeling a field as “temperature” adds ambiguity to the metadata because it is possible that fields can be interpreted differently by the person entering the data and the person reading (and reusing) the data at a later point in time. While it is certainly technically possible to design the form using detailed descriptions of the fields, considering that catalyst test stands frequently change with time, a significant amount of work will go into maintaining the form such that it reflects the current state of the test stand.

Adacta is highly flexible in that it does not rely on forms to categorize information. Instead, Adacta connects data to devices, which has the advantage of recording information in an intuitive manner that mimics how laboratory data are collected. As an example, a classic RDM approach to record catalyst mass involves storing the sample mass along with the measurement date/time in a specific field on a form, such as a spreadsheet. The Adacta way of storing this information is akin to the actual experimental workflow, whereby the sample is placed on a scale, and the scale records a sample weight at a given date/time. The relationship between the sample, the scale, and the data is made within Adacta during the data import process. By storing the sample weight in a manner that associates the data with both the measurement device and the sample, there is no need for a spreadsheet-like form to save results. This relationship between data and devices also makes Adacta extremely flexible. Unlike form-based approaches to RDM, the data structures within Adacta never become outdated as test stands evolve. As well, even though Adacta was developed with the experimental catalysis community in mind, flexibility in how data are stored makes this RDM approach extendable to any type of experimental system where equipment records data in any format.

Ultimately, as databases grow, organizations will be able to continually mine stored information for new insights, both to troubleshoot and to discover breakthroughs in relations that were previously masked and nearly impossible to find. Especially since it is often not known how experimental results will be used in the future, the chance for data re-use is higher if as much metadata are recorded. Unencumbered by the rigidity of forms that can sometimes miss critical metadata, Adacta enhances the promise of Industry 4.0 in the field of catalyst testing by ensuring digital twins are time accurate, with the exact configuration of test equipment accurately saved alongside the measured data.

References

[1] H. Gossler, J. Riedel, E. Daymo, R. Chacko, S. Angeli, O. Deutschmann, CIT, 2022, 94, 1798.